SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants

Understand

In this work, we present SuFIA, the first framework for natural language-guided augmented dexterity for robotic surgical assistants.

• SuFIA incorporates the strong reasoning capabilities of large language models (LLMs) with perception modules to implement high-level planning and low-level control of a robot for surgical sub-task execution.
• This enables a learning-free approach to surgical augmented dexterity without any in-context examples or motion primitives.
• SuFIA uses a human-in-the-loop paradigm by restoring control to the surgeon in the case of insufficient information, mitigating unexpected errors for mission-critical tasks.